Segmentation of iodine brachytherapy implants in fluoroscopy

Purpose

In prostate brachytherapy, intraoperative dosimetry would allow for evaluation of the implant quality while the patient is still in treatment position. Such a mechanism, however, requires 3-D visualization of the deposited seeds relative to the prostate. It follows that accurate and robust seed segmentation is of critical importance in achieving intraoperative dosimetry.

Methods

Implanted iodine brachytherapy seeds are segmented via a region-based implicit active contour model. Overlapping seed groups are then resolved using a template-based declustering technique.

Results

Ground truth seed coordinates were obtained through manual segmentation. A total of 57 clinical C-arm images from 10 patients were used to validate the proposed algorithm. This resulted in two failed images and a 96.0% automatic detection rate with a corresponding 2.2% false-positive rate in the remaining 55 images. The mean centroid error between the manual and automatic segmentations was 1.2 pixels.

Conclusions

Robust and accurate iodine seed segmentation can be achieved through the proposed segmentation workflow.     

Feasibility of respiratory motion-compensated stereoscopic X-ray tracking for bronchoscopy

Purpose

   Precise localization in bronchoscopy is challenging, particularly for peripheral lesions that cannot be reached by conventional bronchoscopes with a large working channel. Existing navigation methods are hampered by respiratory motion, e.g., in the lower lobes. We present an image-guided approach that considers respiratory motion and can localize instruments.

Methods

   We developed a rigid chest marker containing steel balls visible in X-ray images and a pattern for passive tracking with an optical camera system. An experimental setup to evaluate stereoscopic localization and to mimic chest motion was established in our interventional suite. The marker motion was recorded, and X-ray images were acquired from different angles using a standard C-arm. All coordinates were expressed with respect to the stationary tracking camera. The feasibility of motion-compensated stereoscopic localization was assessed.

Results

   The orientation of the C-arm could be established with a mean error of less than $1^{\circ }$ . Triangulation based on two different X-ray images from different angles resulted in a mean error of 1.8 ( $\pm $ 0.7) mm. A similar result was obtained when the marker was moved between X-ray acquisitions, and the mean error was 1.6 ( $\pm $ 1.4) mm. The latencies were approximately 80 and 380 ms for tracking camera and X-ray imaging, respectively. Stereoscopic localization of a moving target was feasible.

Conclusions

   The system presents a flexible alternative for precise stereoscopic localization of a bronchoscope or instruments using a standard C-arm. We demonstrated the ability to track multiple moving markers and to compensate for respiratory motion.     

Cardiovascular and Angiographic Imaging

Purpose

The camera-augmented mobile C-arm (CamC) augments any mobile C-arm by a video camera and mirror construction and provides a co-registration of X-ray with video images. The accurate overlay between these images is crucial to high-quality surgical outcomes. In this work, we propose a practical solution that improves the overlay accuracy for any C-arm orientation by: (i) improving the existing CamC calibration, (ii) removing distortion effects, and (iii) accounting for the mechanical sagging of the C-arm gantry due to gravity.

Methods

A planar phantom is constructed and placed at different distances to the image intensifier in order to obtain the optimal homography that co-registers X-ray and video with a minimum error. To alleviate distortion, both X-ray calibration based on equidistant grid model and Zhang’s camera calibration method are implemented for distortion correction. Lastly, the virtual detector plane (VDP) method is adapted and integrated to reduce errors due to the mechanical sagging of the C-arm gantry.

Results

The overlay errors are 0.38±0.06 mm when not correcting for distortion, 0.27±0.06 mm when applying Zhang’s camera calibration, and 0.27±0.05 mm when applying X-ray calibration. Lastly, when taking into account all angular and orbital rotations of the C-arm, as well as correcting for distortion, the overlay errors are 0.53±0.24 mm using VDP and 1.67±1.25 mm excluding VDP.

Conclusion

The augmented reality fluoroscope achieves an accurate video and X-ray overlay when applying the optimal homography calculated from distortion correction using X-ray calibration together with the VDP.     

Automated aorta segmentation in low-dose chest CT images

Purpose

   Abnormalities of aortic surface and aortic diameter can be related to cardiovascular disease and aortic aneurysm. Computer-based aortic segmentation and measurement may aid physicians in related disease diagnosis. This paper presents a fully automated algorithm for aorta segmentation in low-dose non-contrast CT images.

Methods

   The original non-contrast CT scan images as well as their pre-computed anatomy label maps are used to locate the aorta and identify its surface. First a seed point is located inside the aortic lumen. Then, a cylindrical model is progressively fitted to the 3D image space to track the aorta centerline. Finally, the aortic surface is located based on image intensity information. This algorithm has been trained and tested on 359 low-dose non-contrast CT images from VIA-ELCAP and LIDC public image databases. Twenty images were used for training to obtain the optimal set of parameters, while the remaining images were used for testing. The segmentation result has been evaluated both qualitatively and quantitatively. Sixty representative testing images were used to establish a partial ground truth by manual marking on several axial image slices.

Results

   Compared to ground truth marking, the segmentation result had a mean Dice Similarity Coefficient of 0.933 (maximum 0.963 and minimum 0.907). The average boundary distance between manual segmentation and automatic segmentation was 1.39 mm with a maximum of 1.79 mm and a minimum of 0.83 mm.

Conclusion

   Both qualitative and quantitative evaluations have shown that the presented algorithm is able to accurately segment the aorta in low-dose non-contrast CT images.     

A low-cost tracked C-arm (TC-arm) upgrade system for versatile quantitative intraoperative imaging

Purpose

   C-arm fluoroscopy is frequently used in clinical applications as a low-cost and mobile real-time qualitative assessment tool. C-arms, however, are not widely accepted for applications involving quantitative assessments, mainly due to the lack of reliable and low-cost position tracking methods, as well as adequate calibration and registration techniques. The solution suggested in this work is a tracked C-arm (TC-arm) which employs a low-cost sensor tracking module that can be retrofitted to any conventional C-arm for tracking the individual joints of the device.

Methods

   Registration and offline calibration methods were developed that allow accurate tracking of the gantry and determination of the exact intrinsic and extrinsic parameters of the imaging system for any acquired fluoroscopic image. The performance of the system was evaluated in comparison to an Optotrak \(^\mathrm{TM}\) motion tracking system and by a series of experiments on accurately built ball-bearing phantoms. Accuracies of the system were determined for 2D–3D registration, three-dimensional landmark localization, and for generating panoramic stitched views in simulated intraoperative applications.

Results

   The system was able to track the center point of the gantry with an accuracy of \(1.5 \pm 1.2\)  mm or better. Accuracies of 2D–3D registrations were \(2.3 \pm 1.1\)  mm and \(0.2 \pm 0.2^{\circ }\) . Three-dimensional landmark localization had an accuracy of \(3.1 \pm 1.3\%\) of the length (or \(4.4 \pm 1.9\)  mm) on average, depending on whether the landmarks were located along, above, or across the table. The overall accuracies of the two-dimensional measurements conducted on stitched panoramic images of the femur and lumbar spine were 2.5 \(\pm \) 2.0 % \((3.1 \pm 2.5 \hbox { mm})\) and \(0.3 \pm 0.2^{\circ }\) , respectively.

Conclusion

   The TC-arm system has the potential to achieve sophisticated quantitative fluoroscopy assessment capabilities using an existing C-arm imaging system. This technology may be useful to improve the quality of orthopedic surgery and interventional radiology.     

C-arm angle measurement with accelerometer for brachytherapy: an accuracy study

Purpose

 X-ray fluoroscopy guidance is frequently used in medical interventions. Image-guided interventional procedures that employ localization for registration require accurate information about the C-arm’s rotation angle that provides the data externally in real time. Optical, electromagnetic, and image-based pose tracking systems have limited convenience and accuracy. An alternative method to recover C-arm orientation was developed using an accelerometer as tilt sensor.

Methods

    The fluoroscopic C-arm’s orientation was estimated using a tri-axial acceleration sensor mounted on the X-ray detector as a tilt sensor. When the C-arm is stationary, the measured acceleration direction corresponds to the gravitational force direction. The accelerometer was calibrated with respect to the C-arm’s rotation along its two axes, using a high-accuracy optical tracker as a reference. The scaling and offset error of the sensor was compensated using polynomial fitting. The system was evaluated on a GE OEC 9800 C-arm. Results obtained by accelerometer, built-in sensor, and image-based tracking were compared, using optical tracking as ground truth data.

Results

The accelerometer-based orientation measurement error for primary angle rotation was $-0.1\pm 0.0^{\circ }$ and for secondary angle rotation it was $0.1\pm 0.0^{\circ }$ . The built-in sensor orientation measurement error for primary angle rotation was $-0.1\pm 0.2^{\circ }$ , and for secondary angle rotation it was $0.1\pm 0.2^{\circ }$ . The image-based orientation measurement error for primary angle rotation was $-0.1\pm 1.3^{\circ }$ , and for secondary angle rotation it was $-1.3\pm 0.3^{\circ }$ .

Conclusion

The accelerometer provided better results than the built-in sensor and image-based tracking. The accelerometer sensor is small, inexpensive, covers the full rotation range of the C-arm, does not require line of sight, and can be easily installed to any mobile X-ray machine. Therefore, accelerometer tilt sensing is a very promising applicant for orientation angle tracking of C-arm fluoroscopes.     

Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification

Purpose

Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel’s center, the central vessel reflex. A new method for automatic segmentation was developed and tested.

Method

   A supervised method for retinal vessel segmentation in the images of multi-ethnic school children was developed based on ensemble classifier of bootstrapped decision trees. A collection of dual Gaussian, second derivative of Gaussian and Gabor filters, along with the generalized multiscale line strength measure and morphological transformation is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with the central reflex. The methodology is evaluated on CHASE_DB1, a relatively new public retinal image database of multi-ethnic school children, which is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset.

Results

   The segmented retinal images from the CHASE_DB1 database produced best case accuracy, sensitivity and specificity of 0.96, 0.74 and 0.98, respectively, and worst case measures of 0.94, 0.67 and 0.98, respectively.

Conclusion

   A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.     

Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter

Purpose

Existing computer-aided detection schemes for lung nodule detection require a large number of calculations and tens of minutes per case; there is a large gap between image acquisition time and nodule detection time. In this study, we propose a fast detection scheme of lung nodule in chest CT images using cylindrical nodule-enhancement filter with the aim of improving the workflow for diagnosis in CT examinations.

Methods

Proposed detection scheme involves segmentation of the lung region, preprocessing, nodule enhancement, further segmentation, and false-positive (FP) reduction. As a nodule enhancement, our method employs a cylindrical shape filter to reduce the number of calculations. False positives (FPs) in nodule candidates are reduced using support vector machine and seven types of characteristic parameters.

Results

The detection performance and speed were evaluated experimentally using Lung Image Database Consortium publicly available image database. A 5-fold cross-validation result demonstrates that our method correctly detects 80 % of nodules with 4.2 FPs per case, and detection speed of proposed method is also 4–36 times faster than existing methods.

Conclusion

Detection performance and speed indicate that our method may be useful for fast detection of lung nodules in CT images.     

Needle deflection estimation: prostate brachytherapy phantom experiments

Purpose

The performance of a fusion-based needle deflection estimation method was experimentally evaluated using prostate brachytherapy phantoms. The accuracy of the needle deflection estimation was determined. The robustness of the approach with variations in needle insertion speed and soft tissue biomechanical properties was investigated.

Methods

A needle deflection estimation method was developed to determine the amount of needle bending during insertion into deformable tissue by combining a kinematic deflection model with measurements taken from two electromagnetic trackers placed at the tip and the base of the needle. Experimental verification of this method for use in prostate brachytherapy needle insertion procedures was performed. A total of 21 beveled tip, 18 ga, 200 mm needles were manually inserted at various speeds through a template and toward different targets distributed within 3 soft tissue mimicking polyvinyl chloride prostate phantoms of varying stiffness. The tracked positions of both the needle tip and base were recorded, and Kalman filters were applied to fuse the sensory information. The estimation results were validated using ground truth obtained from fluoroscopy images.

Results

The manual insertion speed ranged from 8 to 34 mm/s, needle deflection ranged from 5 to 8 mm at an insertion depth of 76 mm, and the elastic modulus of the soft tissue ranged from 50 to 150 kPa. The accuracy and robustness of the estimation method were verified within these ranges. When compared to purely model-based estimation, we observed a reduction in needle tip position estimation error by \(52\pm 17\)  % (mean  \(\pm \)  SD) and the cumulative deflection error by \(57\pm 19\)  %.

Conclusions

Fusion of electromagnetic sensors demonstrated significant improvement in estimating needle deflection compared to model-based methods. The method has potential clinical applicability in the guidance of needle placement medical interventions, particularly prostate brachytherapy.     

Local noise estimation in low-dose chest CT images

Purpose

   Image noise in computed tomography (CT) images may have significant local variation due to tissue properties, dose, and location of the X-ray source. We developed and tested an automated tissue-based estimator method for estimating local noise in CT images.

Method

   An automated TBE method for estimating the local noise in CT image in 3 steps was developed: (1) Partition the image into homogeneous and transition regions, (2) For each pixel in the homogeneous regions, compute the standard deviation in a $15\times 15\times 1$ voxel local region using only pixels from the same homogeneous region, and (3) Interpolate the noise estimate from the homogeneous regions in the transition regions. Noise-aware fat segmentation was implemented. Experiments were conducted on the anthropomorphic phantom and in vivo low-dose chest CT scans to validate the TBE, characterize the magnitude of local noise variation, and determine the sensitivity of noise estimates to the size of the region in which noise is computed. The TBE was tested on all scans from the Early Lung Cancer Action Program public database. The TBE was evaluated quantitatively on the phantom data and qualitatively on the in vivo data.

Results

   The results show that noise can vary locally by over 200 Hounsfield units on low-dose in vivo chest CT scans and that the TBE can characterize these noise variations within 5 %. The new fat segmentation algorithm successfully improved segmentation on all 50 scans tested.

Conclusion

   The TBE provides a means to estimate noise for image quality monitoring, optimization of denoising algorithms, and improvement of segmentation algorithms. The TBE was shown to accurately characterize the large local noise variations that occur due to changes in material, dose, and X-ray source location.     

Scaling calibration in region of interest reconstruction with the 1D and 2D ATRACT algorithm

Purpose

   Recently, a reconstruction algorithm for region of interest (ROI) imaging in C-arm CT was published, named Approximate Truncation Robust Algorithm for Computed Tomography (ATRACT). Even in the presence of substantial data truncation, the algorithm is able to reconstruct images without the use of explicit extrapolation or prior knowledge. However, the method suffers from a scaling and offset artifact in the reconstruction. Hence, the reconstruction results are not quantitative. It is our goal to reduce the scaling and offset artifact so that Hounsfield unit (HU) values can be used for diagnosis.

Methods

   In this paper, we investigate two variants of the ATRACT method and present the analytical derivations of these algorithms in the Fourier domain. Then, we propose an empirical correction measure that can be applied to the ATRACT algorithm, to effectively compensate the scaling and offset issue. The proposed method is evaluated on ten clinical datasets in the presence of different degrees of artificial truncation.

Results

   With the proposed correction approach, we achieved an average relative root-mean-square error (rRMSE) of 2.81 % with respect to non-truncated Feldkamp, Davis, and Kress reconstruction, even for severely truncated data. The rRMSE is reduced to as little as 10 % of the image reconstructed without the scaling calibration.

Conclusions

   The reconstruction results show that ROI reconstruction of high accuracy can be achieved since the scaling and offset artifact are effectively eliminated by the proposed method. With this improvement, the HU values may be used for post-processing operations such as bone or soft tissue segmentation if some tolerance is accepted.     

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs

Purpose

   Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs.

Methods

   Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence.

Results

   A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was $0.96\,\hbox {mm} \pm 0.35\,\hbox {mm}$ , requiring $<\!5$  s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference.

Conclusions

   A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.     

Reduction of peristalsis-related gastrointestinal streak artifacts with dual-energy CT: a patient and phantom study

Objective

The purpose of the study was to assess the ability of rapid-kV switching (rs) dual-energy computed tomography (DECT) to reduce peristalsis-related streak artifact.

Methods

rsDECT images of 100 consecutive patients (48 male, 52 female, mean age 57 years) were retrospectively evaluated in this institutional review board–approved study. Image reconstructions included virtual monochromatic 70 and 120 keV images, as well as iodine(-water) and water(-iodine) material decomposition images. We recorded the presence and severity of artifacts qualitatively (4-point scale) and quantitatively [iodine/water concentrations, Hounsfield units, gray scale values (GY)] and compared to corresponding unaffected reference tissue. Similar measures were obtained in DECT images of a peristalsis phantom. Wilcoxon signed-rank and paired t tests were used to compare results between different image reconstructions.

Results

Peristalsis-related streak artifacts were found in 49 (49%) of the DECT examinations. Artifacts were significantly more severe in 70, 120, and water(-iodine) images than in iodine(-water) images (qualitative readout P < 0.001, each). Quantitative measurements were significantly different between the artifact and the reference tissue in 70, 120 keV, and water(-iodine) images (P < 0.001 for both HU and GY for each image reconstruction), but not significantly different in iodine(-water) images (iodine concentrations P = 0.088 and GY P = 0.111). Similar results were seen in the peristalsis DECT phantom study.

Conclusions

Peristalsis-related streak artifacts seen in 70, 120 keV, and water(-iodine) images are substantially reduced in iodine(-water) images at rsDECT.

     

Four-dimensional radiotherapeutic dose calculation using biomechanical respiratory motion description

Purpose

   Organ motion due to patient breathing introduces a technical challenge for dosimetry and lung tumor treatment by hadron therapy. Accurate dose distribution estimation requires patient-specific information on tumor position, size, and shape as well as information regarding the material density and stopping power of the media along the beam path. A new 4D dosimetry method was developed, which can be coupled to any motion estimation method. As an illustration, the new method was implemented and tested with a biomechanical model and clinical data.

Methods

   First, an anatomical model of the lung and tumor was synthesized with deformable tetrahedral grids using computed tomography (CT) images. The CT attenuation values were estimated at the grid vertices. Respiratory motion was simulated biomechanically based on nonlinear finite element analysis. Contrary to classical image-based methods where motion is described using deformable image registration algorithms, the dose distribution was accumulated over tetrahedral meshes that are deformed using biomechanical modeling based on finite element analysis.

Results

   The new method preserves the mass of the objects during simulation with an error between 1.6 and 3.6 %. The new method was compared to an existing dose calculation method demonstrating significant differences between the two approaches and overall superior performance using the new method.

Conclusion

   A unified model of 4D radiotherapy respiratory effects was developed where biomechanical simulations are coupled with dose calculations. Promising results demonstrate that this approach has significant potential for the treatment for moving tumors.     

Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: a preliminary study

Purpose

In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images.

Methods

Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images.

Results

We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0 % with the number of false positives/case at 5.0, and it was 8 % higher than the sensitivity of independent detection systems using CT or PET images alone.

Conclusion

   Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.     

Intraoperative detection and localization of cylindrical implants in cone-beam CT image data

Purpose

Orthopedic fractures are often fixed using metal implants. The correct positioning of cylindrical implants such as surgical screws, rods and guide wires is highly important. Intraoperative 3D imaging is often used to ensure proper implant placement. However, 3D image interaction is time-consuming and requires experience. We developed an automatic method that simplifies and accelerates location assessment of cylindrical implants in 3D images.

Methods

Our approach is composed of three major steps. At first, cylindrical characteristics are detected by analyzing image gradients in small image regions. Next, these characteristics are grouped in a cluster analysis. The clusters represent cylindrical implants and are used to initialize a cylinder-to-image registration. Finally, the two end points are optimized regarding image contrast along the cylinder axis.

Results

A total of 67 images containing 420 cylindrical implants were used for testing. Different anatomical regions (calcaneus, spine) and various image sources (two mobile devices, three reconstruction methods) were investigated. Depending on the evaluation set, the detection performance was between 91.7 and 96.1 % true- positive rate with a false-positive rate between 2.0 and 3.2 %. The end point distance errors ranged from \(1.0 \pm 1.2\) to \(4.3 \pm 2.9\)  mm and the orientation errors from \(1.6 \pm 2.2\) to \(2.3 \pm 2.2\) degrees. The average computation time was less than 5 seconds.

Conclusions

An automatic method was developed and tested that obviates the need for 3D image interaction during intraoperative assessment of cylindrical orthopedic implants. The required time for working with the viewing software of cone-beam CT device is drastically reduced and leads to a shorter time under anesthesia for the patient.     

Video-guided calibration of an augmented reality mobile C-arm

Purpose

The augmented reality (AR) fluoroscope augments an X-ray image by video and provides the surgeon with a real-time in situ overlay of the anatomy. The overlay alignment is crucial for diagnostic and intra-operative guidance, so precise calibration of the AR fluoroscope is required. The first and most complex step of the calibration procedure is the determination of the X-ray source position. Currently, this is achieved using a biplane phantom with movable metallic rings on its top layer and fixed X-ray opaque markers on its bottom layer. The metallic rings must be moved to positions where at least two pairs of rings and markers are isocentric in the X-ray image. The current “trial and error” calibration process currently requires acquisition of many X-ray images, a task that is both time consuming and radiation intensive. An improved process was developed and tested for C-arm calibration.

Methods

Video guidance was used to drive the calibration procedure to minimize both X-ray exposure and the time involved. For this, a homography between X-ray and video images is estimated. This homography is valid for the plane at which the metallic rings are positioned and is employed to guide the calibration procedure. Eight users having varying calibration experience (i.e., 2 experts, 2 semi-experts, 4 novices) were asked to participate in the evaluation.

Results

The video-guided technique reduced the number of intra-operative X-ray calibration images by 89 % and decreased the total time required by 59 %.

Conclusion

A video-based C-arm calibration method has been developed that improves the usability of the AR fluoroscope with a friendlier interface, reduced calibration time and clinically acceptable radiation doses.     

Mesh-based method for measuring intracranial volume in patients with craniosynostosis

Purpose

   Craniosynostosis may lead to reduced intracranial volume (ICV) and disturb normal brain growth and development. Thus, ICV is an important parameter with respect to the surgical outcome. Current methods for ICV determination from computed tomography (CT) images have drawbacks. The aim of this study was to investigate the performance of the novel mesh-based method (MBM) for ICV determination with craniosynostosis patients.

Methods

   Twenty-two patients operated on for scaphocephaly were included in this study. ICVs from preoperative, one-week postoperative, and one-year postoperative CT images were measured with MBM. The level of agreement with the manual segmentation method (MSM) was determined for the measurements of preoperative and one-year postoperative datasets. Repeatability was determined with re-measurements of six datasets. Measurement time was recorded for MBM.

Results

   Mean $(\pm \text{ SD})$ preoperative ICV values were 895.0 $\pm $ 153.1 $\text{ cm}^{3}$ and 896.4 $\pm $ 147.2 $\text{ cm}^{3}$ as measured with MBM and MSM, respectively. Corresponding one-year postoperative values were 1,238.3 $\pm $ 118.7 $\text{ cm}^{3}$ and 1,250.1 $\pm $ 117.5 $\text{ cm}^{3}$ . The MBM allowed ICV determination from one-week postoperative datasets. Measurement time with MBM was 4

Conclusions

   MBM is an efficient method for determining the ICV of craniosynostosis patients, allowing the measurement of skulls with bony defects. The repeatability and short measurement time of MBM are attributable to the user interference and assessment of the measurement process.     

Hybrid MicroPET Imaging for Dosimetric Applications in Mice: Improvement of Activity Quantification in Dynamic MicroPET Imaging for Accelerated Dosimetry Applied to 6-[18 F]Fluoro-l-DOPA and 2-[18 F]Fluoro-l-Tyrosine

Purpose

Dynamic microPET imaging has advantages over traditional organ harvesting, but is prone to quantification errors in small volumes. Hybrid imaging, where microPET activities are cross-calibrated using post scan harvested organs, can improve quantification. Organ harvesting, dynamic imaging and hybrid imaging were applied to determine the human and mouse radiation dosimetry of 6-[18 F]fluoro-l-DOPA and 2-[18 F]fluoro-l-tyrosine and compared.

Procedures

Two-hour dynamic microPET imaging was performed with both tracers in four separate mice for 18 F-FDOPA and three mice for 18 F-FTYR. Organ harvesting was performed at 2, 5, 10, 30, 60 and 120 min post tracer injection with n?=?5 at each time point for 18 F-FDOPA and n?=?3 at each time point for 18 F-FTYR. Human radiation dosimetry projected from animal data was calculated for the three different approaches for each tracer using OLINDA/EXM. S-factors for the MOBY phantom were used to calculate the animal dosimetry.

Results

Correlations between dose estimates based on organ harvesting and imaging was improved from r?=?0.997 to r?=?0.999 for 18 F-FDOPA and from r?=?0.985 to r?=?0.996 (p?<?0.0001 for all) for 18 F-FTYR by using hybrid imaging.

Conclusion

Hybrid imaging yields comparable results to traditional organ harvesting while partially overcoming the limitations of pure imaging. It is an advantageous technique in terms of number of animals needed and labour involved.     

Dual-energy CT of pancreatic adenocarcinoma: reproducibility of primary tumor measurements and assessment of tumor conspicuity and margin sharpness

Purpose

To determine the inter- and intra-reader agreement of size, conspicuity, and margin sharpness of pancreatic adenocarcinoma on monochromatic, polychromatic, and iodine map dual-energy CT (DECT) images.

Methods

Retrospective review of DECT images from 61 patients with untreated pancreatic adenocarcinoma was performed by three radiologists independently. Pancreatic parenchymal phase images were generated as 50 and 70 keV, 140 kVp quality control (QC), and iodine map images. These were analyzed in a blinded randomized order during four reading sessions separated by 5–7 days. For each image set, readers recorded the longest axial and perpendicular primary tumor dimensions, and qualitatively scored tumor conspicuity and edge sharpness on 5-point scales. Linear mixed model was used to estimate and compare tumor measurements, tumor conspicuity, and tumor edge sharpness scores between readers and image sets. Kappa statistics were used to determine inter-observer agreement for tumor conspicuity and edge sharpness.

Results

The range of tumor measures (mean of longest dimension ± standard deviation) was 3.18 ± 1.41 to 3.83 ± 1.57 cm. Reproducibility of tumor measurements was very high with mild variability (s ² = 0.01–0.10) between readers for the different image sets. Inter-observer agreement values for tumor conspicuity (κ = 0.01–0.17) and edge sharpness (κ = 0.12–0.25) were low for all image sets, although two of three readers scored tumor conspicuity and edge sharpness higher on monochromatic and iodine map DECT images than on 140 kVp QC images (p < 0.05).

Conclusions

Pancreatic adenocarcinoma measurements were highly reproducible on DECT images, and subjective reader preference trended toward monochromatic and iodine images rather than polychromatic images.